chromConverter aims to facilitate the conversion of chromatography data from various proprietary formats so it can be easily read into R for further analysis. It currently consists of wrappers around file parsers from various external libraries including Aston, Entab, the ThermoRawFileParser, rainbow, and OpenChrom as well as some parsers written natively in R for (mostly) text-based formats.
Aston/Entab (Entab requires separate installation, see instructions below)
- Agilent ChemStation (
- Agilent MassHunter DAD (
ThermoRawFileParser (requires separate installation, see instructions below)
- Thermo RAW (
chromConverter can now be installed directly from CRAN:
Alternatively, the development version of chromConverter can be installed from GitHub as follows:
or from R Universe:
install.packages("chromConverter", repos="https://ethanbass.r-universe.dev/", type="source")
Some of the parsers rely on external software libraries that must be manually installed.
To install Aston, call the
configure_aston() function to install miniconda along with the necessary python dependencies. Running
read_chroms with the Aston parser selected should also trigger a prompt to install Aston. If you’re running Windows, you may need to install the latest version of ‘Microsoft Visual C++’ if you don’t already have it.
Entab is a Rust-based parsing framework for converting a variety of scientific file formats into tabular data. To use parsers from Entab, you must first install Rust and Entab-R. After following the instructions to install Rust, you can install Entab from GitHub as follows:
devtools::install_github("https://github.com/bovee/entab/", subdir = "entab-r")
Thermo RAW files can be converted by calling the ThermoRawFileParser on the command-line. To install the ThermoRawFileParser, follow the instructions here. If you are running Linux or Mac OS X, you will also need to install mono, following the instructions provided at the link. In addition, when you use chromConverter to convert Thermo RAW files for the first time you will be asked to enter the path to the program.
OpenChrom is opensource chromatography software, containing a large number of file parsers, which can now be conveniently accessed directly from R. Strangely, configuring OpenChrom for use on the command-line deactivates the graphical user interface (GUI). Thus, it is recommended to make a separate copy of OpenChrom if you’d still like to access the GUI. To use the OpenChrom parsers, follow the steps detailed below:
- Download OpenChrom from the website and place it into a directory of your choice.
- If you intend to use the GUI in the future, it is recommended to make a separate copy of OpenChrom for command-line use.
- Follow the instructions to activate OpenChrom’s command-line interface. Alternatively, the command-line option can be activated from R by calling
configure_openchrom_parser(cli="true")or by calling the openchrom_parser and following the prompts.
parser="openchrom". The first time you call the parser, it will ask you to provide the path to your local installation of OpenChrom. The path will then be saved for future use. If the command-line interface is disabled, you will be given the option to automatically activate the command-line.
The workhorse of chromConverter is the
read_chroms function, which functions as a wrapper around all of the supported parsers. To convert files, call
read_chroms, specifying the
paths to a vector of directories or files and the appropriate file format (
format_in). The supported formats include
library(chromConverter) dat <- read_chroms(path, format.in = "chemstation_uv")
read_chroms function will attempt to determine an appropriate parser to use and whether you’ve provided a vector of directories or files. However, if you’d like to be more explicit, you can provide input to the
find_files arguments. Setting
find_files = FALSE will instruct the function that you are providing a vector of files, while
find_files = TRUE implies that you are providing a vector of directories.
If you’d like to automatically export the files, include the argument
export=TRUE along with the path where you’d like to export the files (
path_out). Some parsers (e.g.
ThermoRawFileParser) need to export files for their basic operations. Thus, if these parsers are selected, you will need to specify an argument to
library(chromConverter) dat <- read_chroms(path, find_files = FALSE, path_out="temp", export=TRUE)
For formats where multiple parsers are available, you can choose between them using the
parser argument. For example, Agilent files can now be read using parsers from a number of external libraries, including Aston, Entab, OpenChrom, and rainbow. Some of these parsers must be installed manually as described in the installation instructions further up the page. It is recommended to use the newer Entab or rainbow parsers, since Aston is no longer actively supported.
Parsers in OpenChrom are organized by detector-type. Thus, for the
format_in argument, the user must specify whether the files come from a mass selective detector (
msd), a current-selective detector like a flame-ionization detector (
csd), or a wavelength-selective detector (
wsd), rather than providing a specific file format. In addition, the user should specify what format they’d like to export (
export_format). Current options include
animl (the analytical information markup language). The files will then be converted by calling OpenChrom through the command-line interface. If the files are exported in
csv format, the chromatograms will be automatically read into R. Otherwise, files will be exported to the specified folder but will not be read into the R workspace.
For downstream analyses of chromatographic data, you can also check out my package chromatographR. For interactive visualization of chromatograms, you can check out my new package ShinyChromViewer (alpha release).
You can cite chromConverter as follows:
Bass, E. (2022). chromConverter: chromatographic file converter. http://doi.org/10.5281/zenodo.6792521.
If you use external libraries to convert your files, please cite them as well in published work.
- For tidy extraction of mzML data, see RaMS.